The present invention generally relates to a matching method of enabling optimum correspondence or matching between a plurality of data belonging to two groups and indicative of physical quantities and, more particularly, to a point pattern matching method and system for enabling matching between a plurality of characteristic point coordinates obtained from an object in an image picture and a plurality of predetermined model characteristic point coordinates to realize automatic recognition of the object, and also to a picture recognition method and system using the former method and system.
In the automation of a manufacturing factory, pattern recognition techniques are very important. There have been so far developed many apparatuses which position products or parts and inspect visual patterns of products based on these techniques. These techniques, are intended to realize a simple and high-speed recognition function, based on making the most use of the same shape of products or parts and the invariable ambient atmosphere such as illumination. For example, for the purpose of positioning chips in the course of assembling (wire-bonding) a semiconductor device, a template pattern corresponding to the shape of an electrode pad of the semiconductor device is previously registered and pattern matching is carried out between the obtained image of the semiconductor device and the previously registered template pattern to detect the position of the electrode pad. In this method, however, in the event where different shapes of electrodes of different sorts of chips are employed, it becomes impossible to recognize the electrodes of the chips, so long as the template is not modified. In this way, the prior art recognition method lacks somewhat in flexibility. To overcome it, a model driven image understanding technique has been lately studied for more flexible recognition. The model driven image understanding technique is featured, in short, by previously giving a computer general knowledge on the shape of an object and the spatial relationship between the object and the imaging device, etc. and in carrying out the picture processing, by providing feedback based on comparison and matching between the processing result and the knowledge. Such a technique enables suitable picture processing of even an object different in attitude.
However, this picture understanding technique has a significant matching drawback, which occurs between a group of characteristic points q.sub.i (i: integer between 1 and n) of a model previously stored as knowledge and a group of characteristic points p.sub.i (i: integer between 1 and n) of an object picture obtained from the picture processing. This matching is very important in determining the presence or absence of an object in a picture or the attitude of the object. These characteristic points usually include the characteristic positions of a contour of R object, such as points on the contour line having maximum, minimum and zero curvatures.
When a person conducts matching between a model and its image picture in an example shown in FIG. 11A, the following procedure is carried out. That is, he compares a spatial distribution (which a person usually has as general shape recognition knowledge to an object) of a group of model characteristic points (marked by .largecircle. in the drawing) with a spatial distribution of a group of characteristic points (marked by in the drawing) of the input picture, gives suitable matching between the model characteristic points and the input picture characteristic points, and thereby recognizes every part of the object.
Meanwhile, even when the above matching is realized under the control of a computer, there occurs a rotation between the characteristic points of the model and input picture due to different attitudes or a size difference therebetween due to different distances to the object, which requires such a mechanism as to comparing the general spatial distribution of the input picture characteristic points with that of the model characteristic points. That is, since a series of coordinates of a group of characteristic points of the object input picture obtained through picture processing mean merely positional data ordered at random to the computer, it is necessary to decide matching between a certain coordinate of one of the input picture characteristic points and a corresponding one of the coordinates of the model characteristic points while estimating matching between the other coordinates. The simplest method is to mechanically provide matching between the respective characteristic points of the input picture and all the model characteristic points, to compute errors (which will be referred to as the matching errors, hereinafter) in all pair combinations between the input-picture and model characteristic points, and to select, as a correct combination, one of all the pair combinations between the model characteristic points and the input picture characteristic points which provides the minimum error. However, this method requires (n! combinations for n characteristic points), that a large number of matching combinations be obtained. This in turn requires extended computation time to obtain a solution even if a computer is used. This reason will be explained more in detail. For example, n of the characteristic points are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, . . . A number n! of combinations for each characteristic point are 1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 99168000, 4790020000, . . . As a result, the total processing time requires n! times of one matching error computation time. Even when one matching error computation time is 1 ms and the number of characteristic points is at most 12 for example, the processing time for all the combinations amounts to 479002000 ms=1331 hours. For this reason, in this sort of prior art matching problem, it has been assumed that optimum matching has the matching error which is lower than a predetermined value, so that, when the matching error exceeds the predetermined value in the matching process, the number of matching times is decreased by aborting the matching operation at that moment. This prior art is discussed in a paper entitled "RECOGNITION OF OVERLAPPING OBJECTS USING POINT PATTERN MATCHING ALGORITHM", D-567 in the Spring national conference of the Institute of Electronical Information and Communication Engineers of Japan (1989).